Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS), enhancing road safety and traffic efficiency. While traditional methods have laid foundational work, modern deep learning techniques, particularly transformer-based models and generative approaches, have significantly improved prediction accuracy by capturing complex and non-linear patterns in vehicle motion and traffic interactions. However, these models often overlook the detailed car-following behaviors and inter-vehicle interactions essential for real-world driving scenarios. This study introduces a Cross-Attention Transformer Enhanced Conditional Diffusion Model (Crossfusor) specifically designed for car-following trajectory prediction. Crossfusor integrates detailed inter-vehicular interactions and car-following dynamics into a robust diffusion framework, improving both the accuracy and realism of predicted trajectories. The model leverages a novel temporal feature encoding framework combining GRU, location-based attention mechanisms, and Fourier embedding to capture historical vehicle dynamics. It employs noise scaled by these encoded historical features in the forward diffusion process, and uses a cross-attention transformer to model intricate inter-vehicle dependencies in the reverse denoising process. Experimental results on the NGSIM dataset demonstrate that Crossfusor outperforms state-of-the-art models, particularly in long-term predictions, showcasing its potential for enhancing the predictive capabilities of autonomous driving systems.

翻译：车辆轨迹预测对于推进自动驾驶和高级驾驶辅助系统（ADAS）至关重要，能够提升道路安全与交通效率。传统方法虽奠定了基础工作，但现代深度学习技术，特别是基于Transformer的模型和生成式方法，通过捕捉车辆运动与交通交互中复杂且非线性的模式，显著提高了预测准确性。然而，这些模型常常忽略了现实驾驶场景中至关重要的详细跟驰行为及车辆间交互。本研究提出了一种专门用于跟驰轨迹预测的交叉注意力Transformer增强条件扩散模型（Crossfusor）。Crossfusor将详细的车辆间交互和跟驰动力学整合到一个鲁棒的扩散框架中，从而提高了预测轨迹的准确性和真实性。该模型采用了一种新颖的时间特征编码框架，结合GRU、基于位置的注意力机制和傅里叶嵌入，以捕捉历史车辆动态。它在正向扩散过程中使用由这些编码后的历史特征缩放的噪声，并在反向去噪过程中利用交叉注意力Transformer来建模复杂的车辆间依赖关系。在NGSIM数据集上的实验结果表明，Crossfusor优于现有最先进的模型，尤其是在长期预测方面，展现了其增强自动驾驶系统预测能力的潜力。